One argument often used to quell concerns about the rising energy and resource demand of data centers is that artificial intelligence (AI) models will need less in the future as they improve and become more efficient.
But this seemingly logical thinking is a trap, according to a new United Nations report that quantifies the environmental costs of AI.
The report estimates that by 2030, AI’s energy use could double to consume 3% of the world’s electricity, produce emissions to equal the UK and deplete more water for cooling than the annual drinking water need of the global population.
It also anticipates the use of AI will follow an economic principle known as the “Jevons paradox”, which predicts that when technological improvements increase the efficiency of a resource, it leads to a rise, rather than a fall, in the total consumption of that resource.
The paradox is named after economist William Stanley Jevons who observed this effect with the use of coal in 19th-century England. Efficiency gains did not reduce overall consumption. Instead, the lower costs resulted in expanded use and higher overall demand.
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As AI models become cheaper and more attractive, the report expects this to encourage new uses and higher volumes of use, eroding and possibly erasing any savings from efficiency advances.
To avoid falling into this trap, it lays out a roadmap for responsible AI use based on guiding principles of transparency, efficiency by design, equity and justice, lifecycle responsibility, global cooperation and sustainable use.
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The scale of the problem
Last year, data centers already consumed as much electricity as Saudi Arabia, which ranks as the world’s 11th largest electricity consumer.
If electricity use doubles as projected by 2030, the associated carbon footprint would require 6.7 billion trees grown over ten years to offset this demand.
